Using data science to measure the impact of opioid agonist therapy in patients admitted with Staphylococcus aureus bloodstream infections

使用数据科学测量阿片类激动剂治疗对金黄色葡萄球菌血流感染患者的影响

• 批准号: 10618404
• 负责人: David Goodman
• 金额: $ 20.3万
• 依托单位: UNIVERSITY OF CALIFORNIA LOS ANGELES
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-06-15 至 2024-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10618404
• 关键词: Acute suppurative arthritis due to bacteria; Address; Admission activity; Algorithms; Area; Award; Bacteremia; Bacteria; Bacterial Infections; Behavior; Blood; Brain; Buprenorphine; Cessation of life; Classification; Clinical Research; Coagulation Process; Communicable Diseases; Data; Data Science; Development; Diagnosis; Disease Outcome; Distant; Drug usage; Economic Burden; Electronic Health Record; Embolism; Endocarditis; Epidemic; Equipment; Equipment Contamination; Evidence based practice; Future; Geography; Goals; HIV risk; Health; Health Expenditures; Heart; Hepatitis C; Hepatitis C Transmission; Hospitalization; Hospitals; Immunosuppression; Incidence; Individual; Infection; Injecting drug user; Inpatients; Institution; International Classification of Disease Codes; Investigation; Joints; K-Series Research Career Programs; Kidney; Life; Location; Lung; Machine Learning; Measures; Medical; Medicine; Mentors; Methadone; Methods; Natural Language Processing; Opioid; Outcome; Outpatients; Overdose; Patient Admission; Patient-Focused Outcomes; Patients; Performance; Persons; Pharmaceutical Preparations; Phenotype; Physicians; Population; Practice Guidelines; Predisposition; Productivity; Recording of previous events; Records; Research; Research Personnel; Science; Scientist; Sepsis; Services; Staphylococcus aureus; Statistical Methods; Stream; Testing; Thrombus; Time; Training; Training Programs; Travel; United States; United States National Institutes of Health; Veterans Health Administration; Water; Work; addiction; biomedical data science; blood treatment; career; classification algorithm; cohort; data repository; efficacy testing; electronic data; experience; heroin use; hospital readmission; improved; injection drug use; innovation; machine learning framework; mortality; multidisciplinary; novel; opioid agonist therapy; opioid epidemic; opioid injection; opioid misuse; opioid use; opioid use disorder; opioid user; pathogenic bacteria; pathogenic virus; prescription opioid; responsible research conduct; screening; skin microbiota; substance use treatment; time interval; treatment services; trend; viral transmission

PROJECT SUMMARY/ABSTRACT This career development award will provide early career support for investigation of the management of infec- tious diseases in the setting of addiction in hospitals. The award will provide support for the candidate to develop expertise in the following areas: 1) addiction science research; 2) natural language processing; 3) machine learn- ing; 4) professional development; and 5) responsible conduct of research. For this, Dr. Goodman-Meza will be mentored by a multidisciplinary, cross-institutional team with expertise in addiction, infectious diseases, and data science. His primary mentor, Dr. Steve Shoptaw, has an extensive track record in addiction-related research and training of future independent investigators. His co-mentors include Dr. Alex Bui and Dr. Matthew B. Goetz. Dr. Bui is an expert in biomedical data science and heads NIH training programs in this field. Dr. Goetz has broad experience of productive infectious diseases clinical research within the Veterans Health Administration (VHA). The current opioid epidemic in the United States has been associated with an increase in infections, in particular hepatitis C and bacterial infections. Bacterial infections are the leading infectious diagnosis leading to hospitali- zation in individuals with an opioid use disorder (OUD), and incur significant healthcare expenditures. Despite the availability of opioid agonist therapy (OAT) in the form of methadone or buprenorphine, less than 20% of people with OUD actually receive OAT. Hospitalization for a bacterial infection may be an ideal time to initiate OAT, but the benefits of this practice are unknown. In this proposal, the candidate will assess the impact of initiating OAT in people who inject opioids admitted to the VHA due to a Staphylococcus aureus blood stream infection (bacteremia) – the most common bacterial pathogen among people who inject opioids. Using data already collected for 36,868 cases of S. aureus bacteremia (SAB) from the VHA electronic data repository, the candidate will address three research questions: 1) is a natural language processing algorithm (NLP) more ac- curate than a standard International Classification of Diseases (ICD) code-based approach at screening records to correctly identify individuals who inject opioids in a cohort of patients admitted with SAB; 2) what are the temporal and geographic trends of SAB in people who inject opioids and those who receive OAT at the facility- level; and 3) using a machine learning framework, what are the estimated impacts of OAT on patient centered outcomes – death, readmissions, leaving against medical advice, and subsequent outpatient engagement in OAT. These formative data will help the candidate to establish a productive early career as a physician-scientist and advise development of an OAT-delivery strategy to mitigate infectious complications of injection opioid use. Through this award, Dr. Goodman-Meza will establish himself as an expert physician-scientist at the intersection of infectious disease and addiction, poised to make significant contributions to this important area of medicine.

项目概要/摘要 该职业发展奖将为感染管理的调查提供早期职业支持 医院里成瘾的严重疾病。该奖项将为候选人的发展提供支持 擅长以下领域：1）成瘾科学研究； 2）自然语言处理； 3）机器学习- ing； 4）专业发展； 5) 负责任地进行研究。为此，古德曼-梅萨博士将 由具有成瘾、传染病和数据专业知识的多学科、跨机构团队指导 科学。他的主要导师 Steve Shoptaw 博士在与成瘾相关的研究和研究方面拥有丰富的记录。 培训未来的独立调查员。他的共同导师包括 Alex Bui 博士和 Matthew B. Goetz 博士。博士。 Bui 是生物医学数据科学领域的专家，并负责 NIH 在该领域的培训项目。戈茨博士拥有广泛的 在退伍军人健康管理局 (VHA) 内富有成效的传染病临床研究经验。 美国目前阿片类药物的流行与感染的增加有关，特别是 丙型肝炎和细菌感染。细菌感染是导致住院的主要感染诊断 患有阿片类药物使用障碍 (OUD) 的个体会出现这种情况，并产生大量的医疗支出。尽管 以美沙酮或丁丙诺啡形式的阿片类激动剂治疗 (OAT) 的可用性，不到 20% 患有 OUD 的人实际上接受的是 OAT。因细菌感染住院可能是开始治疗的理想时机 OAT，但这种做法的好处尚不清楚。在此提案中，候选人将评估以下内容的影响： 因金黄色葡萄球菌血流而入住 VHA 的注射阿片类药物的患者开始 OAT 感染（菌血症）——注射阿片类药物的人中最常见的细菌病原体。使用数据 已从 VHA 电子数据存储库收集了 36,868 例金黄色葡萄球菌菌血症 (SAB) 病例， 候选人将解决三个研究问题：1）自然语言处理算法（NLP）更适合 筛选记录时采用基于标准国际疾病分类 (ICD) 代码的方法进行管理 正确识别 SAB 入院患者队列中注射阿片类药物的个体； 2）什么是 注射阿片类药物的人和在机构接受 OAT 的人的 SAB 的时间和地理趋势 - 等级; 3) 使用机器学习框架，OAT 对以患者为中心的估计影响是什么 结果——死亡、再次入院、不顾医疗建议离开以及随后的门诊参与 燕麦。这些形成性数据将帮助候选人作为一名医师科学家建立富有成效的早期职业生涯 并建议制定 OAT 给药策略，以减轻注射阿片类药物使用的感染并发症。 通过该奖项，古德曼-梅萨博士将成为交叉领域的专家医师科学家 传染病和成瘾的研究，有望为这一重要的医学领域做出重大贡献。